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Deep Learning for the New Teacher

In my last post I summarized some of the pedagogical issues I’ve been grappling with as a younger teacher, and today I’d like to share a few thoughts on the notion of deep learning and how that concept might affect the goals I set for my classes. Let me begin by recounting the research narrative of two Arizona State University physicists.

In the early 1980s Ibrahim Abou Halloun and David Hestenes began examining how their students understood the principles of motion. They started by testing undergraduates about to enter a number of introductory physics courses and found, as they expected, that most students’ understanding of motion was “elementary, intuitive … a cross between Aristotelian and 14th-century impetus ideas.”

The results of their “after” tests also confirmed what the professors had feared—that on the deeper, conceptual level, university physics often did little to advance a student’s thinking. While undergraduates memorized the requisite formulas, even many of the top students were still conceptualizing the course’s problems and solutions through their old “intuitive framework.”

The real surprise of the physicists’ study came in exit interviews, when professors challenged students to predict the results of basic experiments. They then performed the procedures and asked the students to observe and comment. What astonished them was that many students attempted to explain away the results of experiments that had upset their expectations. They argued that the experiments had been performed improperly or represented a special case. As Halloun and Hestenes put it, “students held firm to mistaken beliefs even when confronted with phenomena that contradicted those beliefs.”

I came across that story in Ken Bain’s What the Best College Teachers Do, where he uses it to consider how students acquire and, more important, retain new ideas. I like it for a number of reasons, one of which is that it seems to parallel some of the stubborn reluctance of new faculty members (myself included) to think critically about their elementary and intuitive pedagogy.

Robert Boice addresses the issue at some length in The New Faculty Member, where he describes the experience of reading books on teaching with a group of recent faculty hires. While those new professors tended to agree that the texts Boice shared contained good ideas, as a group they felt they needed to spend the bulk of their time developing strong lectures. They would “worry about refinements” later on.

Of course it is true that a professor must know his or her field before sharing it with students, but the consensus on new faculty members seems to be that we spend far too much time on questions of content. Our intuitive pedagogy is based on information delivery rather than knowledge construction. And while that distinction (from Bain) may seem semantic, as I have begun trying to apply it, I’ve found the difference to be significant.

Take, for instance, two strategies for preparing a new course. In the first I asked myself what books I wanted to teach, what content I wanted my students to encounter in my classroom. I built my syllabi by imagining how much I could reasonably cover on a given day. In my first few semesters I would even assign a bit more reading than we could manage, just in case I ran out of material.

The second strategy of course prep begins with the question of what skills I want my students to acquire, what new ideas I want them to engage. Answering that question with a syllabus involves a much more thoughtful consideration of who my students are and where they are likely to begin. Text selection in this second strategy is made in the service of specific goals for the students, and content and practice are combined to fill the daily schedule. Depending on the course, only a small percentage of class time may be spent delivering the content, with the remainder devoted to student application.

As Halloun and Hestenes suggest, it may take significant time to revise our mental models even with the aid of practical application.

One challenge in embracing the second technique is that fresh Ph.D.’s are so comfortable in their content and relatively inexperienced in facilitating application. We haven’t been trained to teach.

Another challenge, perhaps more significant, is that time spent deepening our pedagogy and focusing on student goals can feel like a distraction from the content-saturated scholarship we love. I can’t count how many times I’ve heard younger professors, just this past month, say how happy they were to return to their own work now that the semester is behind them. I probably said it myself.

What questions do you ask yourself as you begin prepping a new course? How important is the notion of deep learning to your pedagogy, and how do you facilitate it in your course design? What other challenges do new teachers face as we attempt to move from strategies of information delivery to those that would help build lasting knowledge?

Gina Stewart has a Ph.D. in organic chemistry from the University of Texas at Austin. She is the chief executive and a founder of Arctic Inc., which develops sustainable methods of weed control for turf and agriculture. She writes about nonacademic careers for Ph.D.'s in the sciences.